Dennis Goldschmidt

Group(s): Neural Control and Robotics
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    Zenker, S. and Aksoy, E E. and Goldschmidt, D. and Wörgötter, F. and Manoonpong, P. (2013).
    Visual Terrain Classification for Selecting Energy Efficient Gaits of a Hexapod Robot. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 577-584. DOI: 10.1109/AIM.2013.6584154.
    BibTeX:
    @inproceedings{zenkeraksoygoldschmidt2013,
      author = {Zenker, S. and Aksoy, E E. and Goldschmidt, D. and Wörgötter, F. and Manoonpong, P.},
      title = {Visual Terrain Classification for Selecting Energy Efficient Gaits of a Hexapod Robot},
      pages = {577-584},
      booktitle = {IEEE/ASME International Conference on Advanced Intelligent Mechatronics},
      year = {2013},
      location = {Wollongong (Australia)},
      month = {Jul 9-12},
      doi = {10.1109/AIM.2013.6584154},
      abstract = {Legged robots need to be able to classify and recognize different terrains to adapt their gait accordingly. Recent works in terrain classification use different types of sensors (like stereovision, 3D laser range, and tactile sensors) and their combination. However, such sensor systems require more computing power, produce extra load to legged robots, and/or might be difficult to install on a small size legged robot. In this work, we present an online terrain classification system. It uses only a monocular camera with a feature-based terrain classification algorithm which is robust to changes in illumination and view points. For this algorithm, we extract local features of terrains using either Scale Invariant Feature Transform (SIFT) or Speed Up Robust Feature (SURF). We encode the features using the Bag of Words (BoW) technique, and then classify the words using Support Vector Machines (SVMs) with a radial basis function kernel. We compare this feature-based approach with a color-based approach on the Caltech-256 benchmark as well as eight different terrain image sets (grass, gravel, pavement, sand, asphalt, floor, mud, and fine gravel). For terrain images, we observe up to 90% accuracy with the feature-based approach. Finally, this online terrain classification system is successfully applied to our small hexapod robot AMOS II. The output of the system providing terrain information is used as an input to its neural locomotion control to trigger an energy-efficient gait while traversing different terrains.}}
    Abstract: Legged robots need to be able to classify and recognize different terrains to adapt their gait accordingly. Recent works in terrain classification use different types of sensors (like stereovision, 3D laser range, and tactile sensors) and their combination. However, such sensor systems require more computing power, produce extra load to legged robots, and/or might be difficult to install on a small size legged robot. In this work, we present an online terrain classification system. It uses only a monocular camera with a feature-based terrain classification algorithm which is robust to changes in illumination and view points. For this algorithm, we extract local features of terrains using either Scale Invariant Feature Transform (SIFT) or Speed Up Robust Feature (SURF). We encode the features using the Bag of Words (BoW) technique, and then classify the words using Support Vector Machines (SVMs) with a radial basis function kernel. We compare this feature-based approach with a color-based approach on the Caltech-256 benchmark as well as eight different terrain image sets (grass, gravel, pavement, sand, asphalt, floor, mud, and fine gravel). For terrain images, we observe up to 90% accuracy with the feature-based approach. Finally, this online terrain classification system is successfully applied to our small hexapod robot AMOS II. The output of the system providing terrain information is used as an input to its neural locomotion control to trigger an energy-efficient gait while traversing different terrains.
    Review:
    Goldschmidt, D. and Hesse, F. and Wörgötter, F. and Manoonpong, P. (2012).
    Biologically inspired reactive climbing behavior of hexapod robots. IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, 4632-4637. DOI: 10.1109/IROS.2012.6386135.
    BibTeX:
    @inproceedings{goldschmidthessewoergoetter2012,
      author = {Goldschmidt, D. and Hesse, F. and Wörgötter, F. and Manoonpong, P.},
      title = {Biologically inspired reactive climbing behavior of hexapod robots},
      pages = {4632-4637},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems IROS},
      year = {2012},
      doi = {10.1109/IROS.2012.6386135},
      abstract = {Insects, e.g. cockroaches and stick insects, have found fascinating solutions for the problem of locomotion, especially climbing over a large variety of obstacles. Research on behavioral neurobiology has identified key behavioral patterns of these animals (i.e., body flexion, center of mass elevation, and local leg reflexes) necessary for climbing. Inspired by this finding, we develop a neural control mechanism for hexapod robots which generates basic walking behavior and especially enables them to effectively perform reactive climbing behavior. The mechanism is composed of three main neural circuits: locomotion control, reactive backbone joint control, and local leg reflex control. It was developed and tested using a physical simulation environment, and was then successfully transferred to a physical six-legged walking machine, called AMOS II. Experimental results show that the controller allows the robot to overcome obstacles of various heights (e.g., 75% of its leg length, which are higher than those that other comparable legged robots have achieved so far). The generated climbing behavior is also comparable to the one observed in cockroaches.}}
    Abstract: Insects, e.g. cockroaches and stick insects, have found fascinating solutions for the problem of locomotion, especially climbing over a large variety of obstacles. Research on behavioral neurobiology has identified key behavioral patterns of these animals (i.e., body flexion, center of mass elevation, and local leg reflexes) necessary for climbing. Inspired by this finding, we develop a neural control mechanism for hexapod robots which generates basic walking behavior and especially enables them to effectively perform reactive climbing behavior. The mechanism is composed of three main neural circuits: locomotion control, reactive backbone joint control, and local leg reflex control. It was developed and tested using a physical simulation environment, and was then successfully transferred to a physical six-legged walking machine, called AMOS II. Experimental results show that the controller allows the robot to overcome obstacles of various heights (e.g., 75% of its leg length, which are higher than those that other comparable legged robots have achieved so far). The generated climbing behavior is also comparable to the one observed in cockroaches.
    Review:
    Goldschmidt, D. and Wörgötter, F. and Manoonpong, P. (2014).
    Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots. Frontiers in Neurorobotics, 1 -- 16, 8, 3. DOI: 10.3389/fnbot.2014.00003.
    BibTeX:
    @article{goldschmidtwoergoettermanoonpong201,
      author = {Goldschmidt, D. and Wörgötter, F. and Manoonpong, P.},
      title = {Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots},
      pages = {1 -- 16},
      journal = {Frontiers in Neurorobotics},
      year = {2014},
      volume= {8},
      number = {3},
      url = {http://journal.frontiersin.org/Journal/10.3389/fnbot.2014.00003/abstract},
      doi = {10.3389/fnbot.2014.00003},
      abstract = {Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal conditioned stimulus, CS and a late, reflex signal unconditioned stimulus, UCS, both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robots leg length in simulation and 75% in a real environment}}
    Abstract: Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal conditioned stimulus, CS and a late, reflex signal unconditioned stimulus, UCS, both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robots leg length in simulation and 75% in a real environment
    Review:
    Manoonpong, P. and Dasgupta, S. and Goldschmidt, D. and Wörgötter, F. (2014).
    Reservoir-based online adaptive forward models with neural control for complex locomotion in a hexapod robot. International Joint Conference on Neural Networks (IJCNN), 3295-3302. DOI: 10.1109/IJCNN.2014.6889405.
    BibTeX:
    @inproceedings{manoonpongdasguptagoldschmidt2014,
      author = {Manoonpong, P. and Dasgupta, S. and Goldschmidt, D. and Wörgötter, F.},
      title = {Reservoir-based online adaptive forward models with neural control for complex locomotion in a hexapod robot},
      pages = {3295-3302},
      booktitle = {International Joint Conference on Neural Networks (IJCNN)},
      year = {2014},
      month = {July},
      doi = {10.1109/IJCNN.2014.6889405},
      abstract = {Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Simulation results show that this bio-inspired approach allows the walking robot to perform complex locomotor abilities including walking on undulated terrains, crossing a large gap, as well as climbing over a high obstacle and a fleet of stairs.}}
    Abstract: Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Simulation results show that this bio-inspired approach allows the walking robot to perform complex locomotor abilities including walking on undulated terrains, crossing a large gap, as well as climbing over a high obstacle and a fleet of stairs.
    Review:
    Manoonpong, P. and Goldschmidt, D. and Wörgötter, F. and Kovalev, A. and Heepe, L. and Gorb, S. (2013).
    Using a Biological Material to Improve Locomotion of Hexapod Robots. Biomimetic and Biohybrid Systems, 402-404, 8064. DOI: 10.1007/978-3-642-39802-5_48.
    BibTeX:
    @incollection{manoonponggoldschmidtwoergoetter201,
      author = {Manoonpong, P. and Goldschmidt, D. and Wörgötter, F. and Kovalev, A. and Heepe, L. and Gorb, S.},
      title = {Using a Biological Material to Improve Locomotion of Hexapod Robots},
      pages = {402-404},
      booktitle = {Biomimetic and Biohybrid Systems},
      year = {2013},
      volume= {8064},
      editor = {Lepora, NathanF. and Mura, Anna and Krapp, HolgerG. and Verschure, PaulF.M.J. and Prescott, TonyJ.},
      language = {English},
      publisher = {Springer Berlin Heidelberg},
      series = {Lecture Notes in Computer Science},
      url = {http://dx.doi.org/10.1007/978-3-642-39802-5_48},
      doi = {10.1007/978-3-642-39802-5_48},
      abstract = {Animals can move in not only elegant but also energy efficient ways. Their skin is one of the key components for this achievement. It provides a proper friction for forward motion and can protect them from slipping on a surface during locomotion. Inspired by this, we applied real shark skin to the foot soles of our hexapod robot AMOS. The material is formed to cover each foot of AMOS. Due to shark skin texture which has asymmetric profile inducing frictional anisotropy, this feature allows AMOS to grip specific surfaces and effectively locomote without slipping. Using real-time walking experiments, this study shows that implementing the biological material on the robot can reduce energy consumption while walking up a steep slope covered by carpets or other felt-like or rough substrates.}}
    Abstract: Animals can move in not only elegant but also energy efficient ways. Their skin is one of the key components for this achievement. It provides a proper friction for forward motion and can protect them from slipping on a surface during locomotion. Inspired by this, we applied real shark skin to the foot soles of our hexapod robot AMOS. The material is formed to cover each foot of AMOS. Due to shark skin texture which has asymmetric profile inducing frictional anisotropy, this feature allows AMOS to grip specific surfaces and effectively locomote without slipping. Using real-time walking experiments, this study shows that implementing the biological material on the robot can reduce energy consumption while walking up a steep slope covered by carpets or other felt-like or rough substrates.
    Review:
    Goldschmidt, D. and Dasgupta, S. and Wörgötter, F. and Manoonpong, P. (2015).
    A Neural Path Integration Mechanism for Adaptive Vector Navigation in Autonomous Agents. International Joint Conference on Neural Networks (IJCNN), neural path integration mechanism for adaptive vector navigation in autonomous agents, 1-8. DOI: 10.1109/IJCNN.2015.7280400.
    BibTeX:
    @inproceedings{goldschmidtdasguptawoergoetter2015,
      author = {Goldschmidt, D. and Dasgupta, S. and Wörgötter, F. and Manoonpong, P.},
      title = {A Neural Path Integration Mechanism for Adaptive Vector Navigation in Autonomous Agents},
      pages = {1-8},
      booktitle = {International Joint Conference on Neural Networks (IJCNN), neural path integration mechanism for adaptive vector navigation in autonomous agents},
      year = {2015},
      month = {July},
      doi = {10.1109/IJCNN.2015.7280400},
      abstract = {Animals show remarkable capabilities in navigating their habitat in a fully autonomous and energy-efficient way. In many species, these capabilities rely on a process called path integration, which enables them to estimate their current location and to find their way back home after long-distance journeys. Path integration is achieved by integrating compass and odometric cues. Here we introduce a neural path integration mechanism that interacts with a neural locomotion control to simulate homing behavior and path integration-related behaviors observed in animals. The mechanism is applied to a simulated six-legged artificial agent. Input signals from an allothetic compass and odometry are sustained through leaky neural integrator circuits, which are then used to compute the home vector by local excitation-global inhibition interactions. The home vector is computed and represented in circular arrays of neurons, where compass directions are population-coded and linear displacements are rate-coded. The mechanism allows for robust homing behavior in the presence of external sensory noise. The emergent behavior of the controlled agent does not only show a robust solution for the problem of autonomous agent navigation, but it also reproduces various aspects of animal navigation. Finally, we discuss how the proposed path integration mechanism may be used as a scaffold for spatial learning in terms of vector navigation.}}
    Abstract: Animals show remarkable capabilities in navigating their habitat in a fully autonomous and energy-efficient way. In many species, these capabilities rely on a process called path integration, which enables them to estimate their current location and to find their way back home after long-distance journeys. Path integration is achieved by integrating compass and odometric cues. Here we introduce a neural path integration mechanism that interacts with a neural locomotion control to simulate homing behavior and path integration-related behaviors observed in animals. The mechanism is applied to a simulated six-legged artificial agent. Input signals from an allothetic compass and odometry are sustained through leaky neural integrator circuits, which are then used to compute the home vector by local excitation-global inhibition interactions. The home vector is computed and represented in circular arrays of neurons, where compass directions are population-coded and linear displacements are rate-coded. The mechanism allows for robust homing behavior in the presence of external sensory noise. The emergent behavior of the controlled agent does not only show a robust solution for the problem of autonomous agent navigation, but it also reproduces various aspects of animal navigation. Finally, we discuss how the proposed path integration mechanism may be used as a scaffold for spatial learning in terms of vector navigation.
    Review:
    Dasgupta, S. and Goldschmidt, D. and Wörgötter, F. and Manoonpong, P. (2015).
    Distributed Recurrent Neural Forward Models with Synaptic Adaptation for Complex Behaviors of Walking Robots. arXiv preprint arXiv:1506.03599.
    BibTeX:
    @article{dasguptagoldschmidtwoergoetter2015,
      author = {Dasgupta, S. and Goldschmidt, D. and Wörgötter, F. and Manoonpong, P.},
      title = {Distributed Recurrent Neural Forward Models with Synaptic Adaptation for Complex Behaviors of Walking Robots},
      journal = {arXiv preprint arXiv:1506.03599},
      year = {2015},
      url = {http://arxiv.org/abs/1506.03599},
      abstract = {Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biome- chanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of in- ternal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively com- bines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of 1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex loco- motive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles.}}
    Abstract: Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biome- chanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of in- ternal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively com- bines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of 1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex loco- motive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles.
    Review:

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